We are interested in the problem of efficiently learning probabilistic deterministic finite automata (PDFA) in the context of the general active MAT-learning framework proposed in [1]} where a learner and a so-called minimum adequate teacher interact by asking and responding questions, respectively. The learner's purpose is to unveil the hidden target automaton only known to the teacher. The latter allows the former to ask two kinds of questions, namely membership (\(\mathbf {MQ}\) ) and equivalence (\(\mathbf {EQ}\) ) queries.
